Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric10
Categorical1

Warnings

target is uniformly distributed Uniform
X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2021-02-12 18:50:09.923183
Analysis finished2021-02-12 19:02:16.412416
Duration12 minutes and 6.49 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.005658359037
Minimum-2.662667417
Maximum3.179546404
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:02:23.770364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.662667417
5-th percentile-1.552387413
Q1-0.6317617759
median-0.0198590123
Q30.6080020101
95-th percentile1.555458981
Maximum3.179546404
Range5.842213821
Interquartile range (IQR)1.239763786

Descriptive statistics

Standard deviation0.9443684423
Coefficient of variation (CV)-166.8979356
Kurtosis0.03169005868
Mean-0.005658359037
Median Absolute Deviation (MAD)0.6231760471
Skewness0.08625621609
Sum-5.658359037
Variance0.8918317548
MonotocityNot monotonic
2021-02-12T14:02:31.458857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.56393404561
 
0.1%
1.2290031611
 
0.1%
0.12361582391
 
0.1%
-0.19573936281
 
0.1%
2.0225983151
 
0.1%
0.47301095811
 
0.1%
-0.87802384351
 
0.1%
-0.55073146791
 
0.1%
0.3098003121
 
0.1%
1.571249891
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.6626674171
0.1%
-2.605393321
0.1%
-2.5159781951
0.1%
-2.4010892291
0.1%
-2.3954909111
0.1%
-2.3199860381
0.1%
-2.2901690761
0.1%
-2.2479349281
0.1%
-2.2278957371
0.1%
-2.2218051151
0.1%
ValueCountFrequency (%)
3.1795464041
0.1%
2.8193285031
0.1%
2.7224893451
0.1%
2.6075932651
0.1%
2.6007002041
0.1%
2.5710097191
0.1%
2.4546859911
0.1%
2.4220434791
0.1%
2.3976181051
0.1%
2.3496173451
0.1%

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.004975139389
Minimum-3.128127687
Maximum2.903815487
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:02:39.538743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.128127687
5-th percentile-1.704359556
Q1-0.7045813124
median-0.02638139597
Q30.684527332
95-th percentile1.77448088
Maximum2.903815487
Range6.031943174
Interquartile range (IQR)1.389108644

Descriptive statistics

Standard deviation1.027555577
Coefficient of variation (CV)-206.5380478
Kurtosis-0.03550379065
Mean-0.004975139389
Median Absolute Deviation (MAD)0.6932958809
Skewness0.05948402393
Sum-4.975139389
Variance1.055870464
MonotocityNot monotonic
2021-02-12T14:02:47.614022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71575543411
 
0.1%
1.1249847221
 
0.1%
0.5061627391
 
0.1%
-2.0220897731
 
0.1%
-1.6909779731
 
0.1%
0.46728644241
 
0.1%
2.4491490481
 
0.1%
-0.12274574941
 
0.1%
0.079557299361
 
0.1%
-0.100262281
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.1281276871
0.1%
-2.7708401881
0.1%
-2.7278635161
0.1%
-2.7208583511
0.1%
-2.6964962981
0.1%
-2.6230401961
0.1%
-2.3566647871
0.1%
-2.3451498971
0.1%
-2.3345317321
0.1%
-2.3336823751
0.1%
ValueCountFrequency (%)
2.9038154871
0.1%
2.849999111
0.1%
2.819389911
0.1%
2.7494494941
0.1%
2.6947444291
0.1%
2.678712821
0.1%
2.6690703471
0.1%
2.6636187181
0.1%
2.5758622411
0.1%
2.5420680871
0.1%

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008798046656
Minimum-3.038309224
Maximum3.064032393
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:02:55.742669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.038309224
5-th percentile-1.635680917
Q1-0.691151393
median-0.001831664975
Q30.6678442046
95-th percentile1.695623876
Maximum3.064032393
Range6.102341618
Interquartile range (IQR)1.358995598

Descriptive statistics

Standard deviation0.9912355603
Coefficient of variation (CV)-112.665413
Kurtosis-0.09759858864
Mean-0.008798046656
Median Absolute Deviation (MAD)0.6849867322
Skewness-0.004088722797
Sum-8.798046656
Variance0.982547936
MonotocityNot monotonic
2021-02-12T14:03:03.401818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.1976143841
 
0.1%
0.0286335681
 
0.1%
-1.3302263731
 
0.1%
0.74102052181
 
0.1%
-0.81271818611
 
0.1%
0.19465869281
 
0.1%
0.6084042761
 
0.1%
1.2760649361
 
0.1%
1.4269732681
 
0.1%
-0.01640256721
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0383092241
0.1%
-2.9229986321
0.1%
-2.7118505681
0.1%
-2.6385001291
0.1%
-2.616894611
0.1%
-2.4764919921
0.1%
-2.3819111881
0.1%
-2.3349426611
0.1%
-2.3304575391
0.1%
-2.2909288381
0.1%
ValueCountFrequency (%)
3.0640323931
0.1%
3.0313442121
0.1%
2.7783043781
0.1%
2.6291282941
0.1%
2.4607476611
0.1%
2.299665651
0.1%
2.2413167291
0.1%
2.2163164981
0.1%
2.2042111981
0.1%
2.1991215681
0.1%

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0003874407777
Minimum-2.977259705
Maximum2.915900373
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:03:11.039288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.977259705
5-th percentile-1.618147748
Q1-0.6670029199
median-0.01266179157
Q30.6523075876
95-th percentile1.630852319
Maximum2.915900373
Range5.893160078
Interquartile range (IQR)1.319310508

Descriptive statistics

Standard deviation0.980365856
Coefficient of variation (CV)2530.363122
Kurtosis-0.0915838222
Mean0.0003874407777
Median Absolute Deviation (MAD)0.659862552
Skewness0.04681474442
Sum0.3874407777
Variance0.9611172115
MonotocityNot monotonic
2021-02-12T14:03:18.618993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98274319641
 
0.1%
-1.5346384071
 
0.1%
-0.52754536161
 
0.1%
-0.028031584661
 
0.1%
0.59040131981
 
0.1%
-0.38415865771
 
0.1%
0.18664685691
 
0.1%
-0.91205327741
 
0.1%
1.3378279021
 
0.1%
-0.68402972281
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.9772597051
0.1%
-2.763044571
0.1%
-2.7000340621
0.1%
-2.4019806181
0.1%
-2.3741057551
0.1%
-2.3588444811
0.1%
-2.3178359151
0.1%
-2.3154885111
0.1%
-2.2936159431
0.1%
-2.2722119621
0.1%
ValueCountFrequency (%)
2.9159003731
0.1%
2.7649266571
0.1%
2.7364197021
0.1%
2.7093415781
0.1%
2.6543772641
0.1%
2.558822931
0.1%
2.4538347171
0.1%
2.4485806911
0.1%
2.2769830061
0.1%
2.2656360781
0.1%

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03243640701
Minimum-3.06530595
Maximum3.147222969
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:03:26.250231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.06530595
5-th percentile-1.575007659
Q1-0.6250181168
median0.04685884736
Q30.7186728263
95-th percentile1.626370861
Maximum3.147222969
Range6.212528919
Interquartile range (IQR)1.343690943

Descriptive statistics

Standard deviation0.9859881998
Coefficient of variation (CV)30.39757762
Kurtosis-0.04373982012
Mean0.03243640701
Median Absolute Deviation (MAD)0.6725124572
Skewness-0.04214376568
Sum32.43640701
Variance0.9721727301
MonotocityNot monotonic
2021-02-12T14:03:34.300372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6064847941
 
0.1%
-0.30675891541
 
0.1%
-0.29524186891
 
0.1%
0.28988026161
 
0.1%
-0.39269656971
 
0.1%
-1.1222304381
 
0.1%
1.0119860191
 
0.1%
0.13376636031
 
0.1%
-1.1965090561
 
0.1%
0.13117745221
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.065305951
0.1%
-2.8898505261
0.1%
-2.6814501691
0.1%
-2.6179567231
0.1%
-2.6069678161
0.1%
-2.5299350611
0.1%
-2.4956118551
0.1%
-2.4812264381
0.1%
-2.45549561
0.1%
-2.4417298271
0.1%
ValueCountFrequency (%)
3.1472229691
0.1%
2.9790256591
0.1%
2.9568421021
0.1%
2.7667486751
0.1%
2.5661381461
0.1%
2.433207661
0.1%
2.3432999641
0.1%
2.3410840581
0.1%
2.3382962411
0.1%
2.3348251241
0.1%

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02966117007
Minimum-3.153572112
Maximum3.003858103
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:03:41.918625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.153572112
5-th percentile-1.549621568
Q1-0.661072853
median0.06935898222
Q30.6650243991
95-th percentile1.65062368
Maximum3.003858103
Range6.157430215
Interquartile range (IQR)1.326097252

Descriptive statistics

Standard deviation0.9949563487
Coefficient of variation (CV)33.54406945
Kurtosis0.08064109598
Mean0.02966117007
Median Absolute Deviation (MAD)0.6563247405
Skewness-0.06995984219
Sum29.66117007
Variance0.9899381357
MonotocityNot monotonic
2021-02-12T14:03:49.436242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.82968717571
 
0.1%
0.8615767361
 
0.1%
0.044096911461
 
0.1%
-0.38944628421
 
0.1%
-1.0916931881
 
0.1%
-1.1072450651
 
0.1%
0.10941872271
 
0.1%
1.4321357691
 
0.1%
-0.16578197611
 
0.1%
1.8199562841
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.1535721121
0.1%
-3.0235306541
0.1%
-3.0141049721
0.1%
-2.9948495561
0.1%
-2.9695834891
0.1%
-2.8893529291
0.1%
-2.6561312731
0.1%
-2.5502945971
0.1%
-2.5367911
0.1%
-2.4180322171
0.1%
ValueCountFrequency (%)
3.0038581031
0.1%
2.7911313181
0.1%
2.6760223291
0.1%
2.6656665421
0.1%
2.5938657761
0.1%
2.5805157661
0.1%
2.4944578281
0.1%
2.4888949011
0.1%
2.4258848761
0.1%
2.3559477231
0.1%

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01455595588
Minimum-3.028176593
Maximum3.282209802
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:03:57.462385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.028176593
5-th percentile-1.596017287
Q1-0.6487722618
median0.01468085618
Q30.6816431832
95-th percentile1.586230459
Maximum3.282209802
Range6.310386395
Interquartile range (IQR)1.330415445

Descriptive statistics

Standard deviation0.9853747042
Coefficient of variation (CV)67.69563694
Kurtosis0.1059343314
Mean0.01455595588
Median Absolute Deviation (MAD)0.6661304673
Skewness0.07278843432
Sum14.55595588
Variance0.9709633078
MonotocityNot monotonic
2021-02-12T14:04:05.414109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.205168811
 
0.1%
-0.33679609471
 
0.1%
0.063890244141
 
0.1%
-0.17106891721
 
0.1%
-0.62727901441
 
0.1%
0.04106294161
 
0.1%
0.11327013571
 
0.1%
-0.39743669551
 
0.1%
-1.2207419561
 
0.1%
-0.16691722241
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0281765931
0.1%
-2.8283623481
0.1%
-2.5661767481
0.1%
-2.4906011781
0.1%
-2.3966999911
0.1%
-2.3773502751
0.1%
-2.3659633181
0.1%
-2.3532922881
0.1%
-2.3421123991
0.1%
-2.299345781
0.1%
ValueCountFrequency (%)
3.2822098021
0.1%
3.2301919351
0.1%
3.1291183341
0.1%
2.9618255951
0.1%
2.9295541981
0.1%
2.8508924211
0.1%
2.6664188791
0.1%
2.6596616511
0.1%
2.5790877471
0.1%
2.4916897491
0.1%

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03015787524
Minimum-3.587631028
Maximum2.878682808
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:04:13.329535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.587631028
5-th percentile-1.612669581
Q1-0.7093760482
median-0.01730682502
Q30.5992913557
95-th percentile1.602157085
Maximum2.878682808
Range6.466313836
Interquartile range (IQR)1.308667404

Descriptive statistics

Standard deviation0.9694697479
Coefficient of variation (CV)-32.14648711
Kurtosis-0.03778836627
Mean-0.03015787524
Median Absolute Deviation (MAD)0.6475908882
Skewness0.006950814197
Sum-30.15787524
Variance0.9398715921
MonotocityNot monotonic
2021-02-12T14:04:20.942315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.371988141
 
0.1%
-0.33307893061
 
0.1%
0.3334771131
 
0.1%
0.48028287981
 
0.1%
0.53704783631
 
0.1%
0.02644494851
 
0.1%
0.21644731891
 
0.1%
1.1994561731
 
0.1%
1.3305238031
 
0.1%
0.55936509571
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.5876310281
0.1%
-2.8088218191
0.1%
-2.7485761721
0.1%
-2.5971863731
0.1%
-2.5060296541
0.1%
-2.4552729551
0.1%
-2.4171594011
0.1%
-2.347040861
0.1%
-2.3016872261
0.1%
-2.2339132461
0.1%
ValueCountFrequency (%)
2.8786828081
0.1%
2.8698848481
0.1%
2.5361921781
0.1%
2.5168390471
0.1%
2.4632967591
0.1%
2.4177217051
0.1%
2.3979650551
0.1%
2.318140761
0.1%
2.2698491991
0.1%
2.2467643951
0.1%

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04811519454
Minimum-2.784007137
Maximum3.089931844
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:04:28.517993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.784007137
5-th percentile-1.640990753
Q1-0.6145091564
median0.06804239147
Q30.712224459
95-th percentile1.620004297
Maximum3.089931844
Range5.873938981
Interquartile range (IQR)1.326733615

Descriptive statistics

Standard deviation0.9883666726
Coefficient of variation (CV)20.54167466
Kurtosis-0.1019950364
Mean0.04811519454
Median Absolute Deviation (MAD)0.6737590922
Skewness-0.09089751458
Sum48.11519454
Variance0.9768686796
MonotocityNot monotonic
2021-02-12T14:04:36.272101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.97481116851
 
0.1%
0.72961225711
 
0.1%
-1.1976873871
 
0.1%
1.7135085851
 
0.1%
1.0527685521
 
0.1%
-1.3693266281
 
0.1%
-0.051371641831
 
0.1%
-1.690814061
 
0.1%
1.0058048611
 
0.1%
-1.8145328961
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.7840071371
0.1%
-2.7447224971
0.1%
-2.7070234521
0.1%
-2.6288617841
0.1%
-2.6042412321
0.1%
-2.5956165961
0.1%
-2.529274541
0.1%
-2.5290708831
0.1%
-2.4671502751
0.1%
-2.3856720741
0.1%
ValueCountFrequency (%)
3.0899318441
0.1%
3.0510969261
0.1%
2.9457663071
0.1%
2.6525897381
0.1%
2.5189183751
0.1%
2.3916473481
0.1%
2.3497475781
0.1%
2.3365540371
0.1%
2.3318767181
0.1%
2.3180657761
0.1%

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01996128522
Minimum-2.793918507
Maximum2.61995847
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T14:04:43.986701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.793918507
5-th percentile-1.578658864
Q1-0.6372780846
median-0.05077619076
Q30.6377260355
95-th percentile1.540283779
Maximum2.61995847
Range5.413876977
Interquartile range (IQR)1.27500412

Descriptive statistics

Standard deviation0.9492936693
Coefficient of variation (CV)-47.55674091
Kurtosis-0.195318099
Mean-0.01996128522
Median Absolute Deviation (MAD)0.6384844398
Skewness-0.005268893419
Sum-19.96128522
Variance0.9011584706
MonotocityNot monotonic
2021-02-12T14:04:51.731747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.0798022711
 
0.1%
-0.96319230891
 
0.1%
0.73160171581
 
0.1%
0.38841186311
 
0.1%
-0.98501104651
 
0.1%
-0.21554038081
 
0.1%
0.42156050841
 
0.1%
-0.21224639851
 
0.1%
-0.030013389541
 
0.1%
1.4707082681
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.7939185071
0.1%
-2.7713693711
0.1%
-2.703704631
0.1%
-2.643423321
0.1%
-2.3930133141
0.1%
-2.3564822521
0.1%
-2.3269028821
0.1%
-2.2992375351
0.1%
-2.2964529721
0.1%
-2.2380659531
0.1%
ValueCountFrequency (%)
2.619958471
0.1%
2.5955309131
0.1%
2.5727473991
0.1%
2.5572090251
0.1%
2.3379073661
0.1%
2.3280564051
0.1%
2.2878083121
0.1%
2.2598705621
0.1%
2.0546630421
0.1%
2.0290356091
0.1%

target
Categorical

UNIFORM

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
0500
50.0%
1500
50.0%
2021-02-12T14:05:07.446343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T14:05:15.336778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring characters

ValueCountFrequency (%)
0500
50.0%
1500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

ValueCountFrequency (%)
0500
50.0%
1500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

ValueCountFrequency (%)
0500
50.0%
1500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ValueCountFrequency (%)
0500
50.0%
1500
50.0%

Interactions

2021-02-12T13:50:19.124413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:50:26.953010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:50:35.235363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:50:44.020943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:50:51.795122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:50:59.871546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:51:08.250741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:51:16.332729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:51:24.117085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:51:32.158917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:51:39.810808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:51:48.158816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:51:55.734426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:52:03.331976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:52:11.656346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:52:19.906959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:52:28.155122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:52:36.343419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:52:44.042846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:52:52.016905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:52:59.694652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:53:07.435081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:53:15.470534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:53:23.176107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:53:31.734267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:53:39.294306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:53:47.113365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:53:55.019903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:54:03.130508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:54:11.157980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:54:18.706905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:54:26.655622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:54:34.577998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:54:42.374414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:54:50.096666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:54:58.414548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:55:06.074301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:55:13.546774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:55:21.227372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:55:28.927421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:55:37.059666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:55:44.632797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:55:53.034946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:56:01.443492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:56:09.115246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:56:16.184067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:56:23.567105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:56:31.662622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:56:39.235486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:56:46.827174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:56:54.603311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:57:02.375626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:57:10.099803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:57:17.607011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:57:25.815270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:57:33.883446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:57:41.707644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:57:49.290900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:57:56.968138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:58:04.603536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:58:12.207032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:58:19.834206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:58:27.131075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:58:34.773946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:58:42.611841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:58:50.302669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:58:58.383084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:59:06.023257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:59:16.287176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:59:23.822757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:59:31.966415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:59:39.764281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:59:47.418851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T13:59:55.486224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:00:03.715514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:00:11.638757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:00:19.391705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:00:27.091262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:00:34.914769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:00:43.254348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:00:50.980983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:00:58.719447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:01:06.511329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:01:14.062076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:01:22.040474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:01:29.766416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:01:37.786181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:01:45.407690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:01:53.042151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T14:02:00.746531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-12T14:05:23.011573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T14:05:31.010244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T14:05:39.107652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T14:05:46.839704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-12T14:02:08.522970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T14:02:16.107904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X0X1X2X3X4X5X6X7X8X9target
0-0.7693810.003561-0.4106150.363195-1.718970-0.593679-0.168739-1.1030610.5683291.0651760
10.313750-0.3269811.9478920.982743-2.129539-0.604825-1.1702000.2030810.058242-0.2165870
2-0.9420600.6689250.071957-0.5295520.509740-1.7674960.7468501.7844371.2467431.5344730
30.2840191.126219-0.2527160.8837751.616811-0.337845-0.610654-0.7803580.868431-0.3720221
4-0.2337480.792365-0.8738172.243795-0.0684831.262400-1.3577382.8698850.6329771.9827520
5-0.994444-1.052129-0.7387250.455242-0.1818910.4416160.161147-0.6044700.3595500.4351320
60.120096-0.485041-1.1229100.211837-0.5338520.5374101.088889-0.224640-0.953360-1.1250150
7-1.298814-0.283114-1.487896-0.156856-0.7621920.1249190.0897342.8786830.778565-1.0656641
80.523573-0.657526-1.8881351.177957-0.2061490.491394-0.180734-1.493760-0.340842-1.4180110
9-0.321108-0.169517-0.077913-0.5275940.4624730.9383220.984203-0.3688251.181787-0.0554460

Last rows

X0X1X2X3X4X5X6X7X8X9target
990-0.310075-0.2876900.2432660.563441-0.127345-0.249052-0.140552-1.198909-0.9494020.9019710
991-0.721965-0.0711830.253556-1.7649240.1968170.524917-0.0029850.413713-0.874551-0.0687270
9920.8274031.480896-0.3737430.116852-0.9884031.5122550.0410630.3795280.5849510.7507890
9930.3626590.246774-1.663069-1.1366450.443918-0.666999-0.0812081.665861-1.643292-0.1841940
994-2.1977741.115293-1.482134-1.241888-1.2663490.789319-0.0721200.730503-0.7266661.6875831
9950.896118-0.508230-0.414584-0.321946-0.355538-0.569138-0.364805-1.3026740.3091270.3393871
996-0.029942-0.451437-1.1834341.2133140.401253-0.0259540.298979-1.1253450.767207-0.0039411
997-0.2123281.114725-0.1347861.0701820.6476230.7977950.9540640.105352-1.1982080.3612271
9980.7621700.7767150.1780321.132147-0.919297-1.0086332.9295540.6185161.068035-0.9634510
9990.383900-0.853172-0.896608-0.860084-0.276559-0.6931720.210867-0.6919020.1761921.4982781